Welcome to my website!

I (Eli) Chien (簡翌 in Chinese)

Email: ichien6 at gatech dot edu

WeChat: elichien1234

(My origami of dragon as the icon :) )

CV (last update 2025-01-09)

Google Scholar X(Twitter) BlueSky

Short Bio

I am a visiting researcher at Google and an incoming Assistant Professor at National Taiwan University (Electrical Engineering). Previously, I was a Postdoctoral Fellow in the School of Electrical and Computer Engineering at Georgia Institute of Technology, where I am very fortunate to work with Prof. Pan Li. I received my Ph.D. degree in Electrical and Computer Engineering at the University of Illinois Urbana - Champaign. I am very fortunate to work with my advisor Prof. Olgica Milenkovic. Before that, I got my B.S. degree in Electrical Engineering from National Taiwan University where I worked with Prof. I-Hsiang Wang.

My research interests lie in the field of Regulatable AI, in particular privacy and graph-related machine learning. I currently focus on machine unlearning, differential privacy, AI copyright infringement, and their application to graph ML. My other works include geometric learning, including general (hyper)graph neural networks, active learning on (hyper)graphs, and classification in hyperbolic/product spaces.

I spent two summers in Amazon Search and worked with Wei-Cheng Chang, Cho-Jui Hsieh, Jiong Zhang, Jyun-Yu Jiang, Hsiang-fu Yu, and Inderjit Dhillon. Our works are about extracting high-quality node features from raw data with the help of graph information (paper accepted by ICLR 2022) and improving eXtreme multilabel classification methods via side-information with graph learning techniques (paper accepted by ICML 2023). I spent a summer in Nokia Bell Labs and worked with Antonia Maria Tulino and Jaime Llorca. Our work is about active learning on the geometric block model (paper accepted by AAAI 2019).

I've been invited (previously or currently) as an area chair of NeurIPS, and a reviewer of NeurIPS, ICML, ICLR, AISTATS, KDD, TMLR, TheWebConf, SDM, ISIT, CoLLAs, TPDP.

Collaborations: Feel free to drop me an email if you would like to work with me!

For prospective students: Note that I am joining NTU starting February 2026, so I will not be able to officially advise you before that date. Please stay tuned for more information around the end of 2025.

News

2025/07: I will be at the ICML main conference, presenting our work on LLM unlearning evaluation (see below). We also have a work on AI copyright to be presented in the MemFM and R2FM workshop this year. Let me know if you would like to chat!
2025/06: I started as a visiting researcher at Google (Fremont, SEA), working with Peter Kairouz and Zheng Xu!
2025/06: Can Privacy Amplification By Iteration (PABI) be applied to the zeroth-order method? What needs to be taking care of, main challenges and solutions? Check out our latest preprint "Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States" for more details! Joint work with Wei-Ning Chen and Pan Li.
2025/05: Our paper "Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning" is accepted by ICML 2025! Congrats to the leading author, Rongzhe, and all my collaborators! This is my first "last" author paper, so it feels quite special to me :)
2025/04: I will serve as an area chair for NeurIPS 2025!
2025/03: I will join National Taiwan University as an Assistant Professor starting in February 2026! I really appreciate everyone who has helped me during my job search journey!
2025/03: I will give a talk at School of CSE Seminar Series at GaTech on the topic: "Machine Unlearning: The General Theory and LLM Practice of Privacy"!
2025/01: I will serve as a senior reviewer for CoLLAs 2025!
2025/01: Two papers accepted by ICLR 2025!
- Convergent privacy loss of noisy-sgd without convexity and smoothness: This is the one I lead, collaborating with Pan. I really appreciate the valuable comments provided by reviewers, AC, and my friends.
- LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation (Spotlight): This one is led by Mufei. Big congrats to him and all the collaborators!
2024/11: I gave a talk at the National Taiwan University EE department on the topic: Machine Unlearning: the General Theory and LLM Practice for Privacy! Really nice to be back to NTUEE, reminds me of many good old times.
2024/09: Three papers about privacy theory (Unlearning, Graph DP) accepted by NeurIPS 2024! Thanks to all my collaborators and congrats to Rongzhe for leading the DP PageRank paper!